Advances in Momentum Trading Strategies

Advances in Momentum Trading Strategies | Udemy
English | Size: 2.83 GB
Genre: eLearning

Delve Deep into the World of Advanced Momentum Trading

What you’ll learn
Master Momentum Profits: Explore a century of profitable trend-following strategies and their evolution.
Unlock Momentum Turning Points: Learn to detect and profit from key market changes.
Exploit different volatility regimes, to dynamically swap between fast and slow parameters, to increase profits and improve the sharpe ratio.
Smart Position Sizing: Use Volatility Targeting to enhance Sharpe Ratio and returns across assets.
Learn how natural language processing (NLP) is used on news sources to construct sentiment signals and how to build time series momentum strategies.
Deep Momentum Strategies: Discover advanced time-series tactics using deep learning.
Rank with Precision: Apply Learning to Rank algorithms for superior cross-sectional momentum strategies.
Forecast with Insight: Integrate crucial features into ML models for more accurate market predictions.

Advances in Momentum Trading Strategies is a comprehensive and in-depth course designed for graduate-level students and seasoned professionals. This course offers a unique blend of theory, practical application, and cutting-edge research, enabling participants to master the intricacies of momentum trading across various market conditions.

Course Sections:

A Century of Evidence on Trend-Following Investing: Explore the historical performance and methodology of trend-following strategies over a century, including during crises and different economic environments.

Momentum Turning Points: Unravel the concept of turning points in momentum trading. Learn about dynamic versus static strategies, and the impact of noise and persistence on signal quality.

Trending Fast and Slow: Delve into the theory and application of varying speed (window periods) in trend analysis. Discover the role of risk management and the statistics of S&P 500 in momentum strategies.

Position Sizing: Volatility Targeting: Understand the impact of volatility targeting on position sizing across asset classes, and why this approach is effective.

Deep Momentum Networks (Time Series Momentum Strategies): Learn about enhancing time-series momentum strategies using deep neural networks, including the construction of trading signals and performance evaluation.

Advanced Deep Momentum Networks with Change Point Detection: Explore the integration of change point detection in deep momentum networks, examining methodology and results.

Cross-Sectional Momentum Strategies with Learning to Rank: Gain insights into building cross-sectional systematic strategies using Learning to Rank (LTR), including Python library implementation for LambdaMart.

Market Conditions that Favor Strategies: Analyze various investment strategies like carry, momentum, and value in different market conditions. Learn about signal and portfolio construction.

Enhancing Cross-Sectional Strategies by Context-Aware LTR with Self-Attention: Understand how to enhance ranking in cross-sectional momentum strategies using context-aware models and transformer architecture.

Why This Course?

Whether you’re a graduate student specializing in financial engineering, machine learning, applied mathematics, or a professional quant trader or analyst, this course will elevate your understanding and application of momentum trading strategies. It’s not just a course; it’s an investment in your future in the dynamic world of trading.

Who this course is for:
This course is NOT for beginners! Its an advanced course aimed at graduate level students and industry professionals.
Ambitious Graduate Students: Particularly those in Machine Learning, Applied Mathematics, Financial Engineering, and Computer Science, looking for a challenge.
Aspiring Quant Traders and Analysts: If you’re eager to craft your own momentum-based trading strategies, this course is your launchpad.
Experienced Traders: Enhance your skill set with in-depth knowledge of cross-sectional and time-series momentum strategies.

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